In order to determine a characteristic of an optical communication channel, channel measurements must be performed. The characteristic of the optical communication channel is not only influenced by the characteristic of the light propagation path but also by the characteristics of optical components.
Characterization of linear (passive) optical components is often performed by conventional measurements of the passive optical components using an optical spectrum analyzer or a laser scanning system with a focus on magnitude or intensity response. Phase responses are typically measured by modulation phase shift methods, and the interferometric methods. Combining the magnitude and phase response, a transfer function can fully characterize a passive optical component.
Further, components with very fine structures in the wavelength domain are sensitive to mechanical vibrations or thermal fluctuations. Therefore resolution and measurement speed are important specifications to design or choose a characterization method. The methods based on the frequency sweeper using single sideband modulation have demonstrated high-resolution and fast measurement speed. However, high-resolution frequency sweeping over a GHz bandwidth is a slow process, which is not suitable for fast measurements above a kHz range. In addition time varying channel impairments like state-of-polarization rotation (SOP rotation), which may result from mechanical vibrations in the 100 kHz range, cannot be measured over a wide bandwidth with classical sweeping methods, since the sweeping time is too slow. Further, it cannot be obtained if polarization-changes behave differently for different spectral components, which is relevant for the equalization strategy.
For adaptive equalization in digital coherent receivers, “blind” non-training-aided (NTA) and training-aided (TA) methods can be performed to update and to converge the filtering function. TA channel estimation adds a training sequence (TS) to the customer payload data, which is repeated at a regular rate fast enough to track time-varying channel distortions. In a data transmission link, adding training information to the signal degrades the spectral efficiency. The training overhead should be kept considerably low, e.g. below 3% of total signal capacity. With the aid of the spectra of the received TS and the known transmitted spectra of the transmitted TS, full and instantaneous channel estimation can be performed.
The channel estimation can be employed to calculate different filter solutions for adaptive equalization, or cab be used to initialize static device imperfection compensation or to calibrate components and devices during production. It represents amplitude, phase and polarization information of the estimated channel. Only linear channel transfer functions can be estimated.
The channel can consist of optical fiber, filters or any optical components (and electrical components). Also active optical components like amplifiers could be partly characterized and can be tolerated within the channel estimation of a multi-span link. In particular passive optical components like fiber Bragg gratings, interferometers or 90-deg hybrids work on wide-bandwidths. Their frequency-dependent group delay, ripple and attenuation need to be measured in order to define their quality.
For network planning and optimization the link budget including OSNR margin, quality of fiber, amplifiers and other network elements is required to be estimated. This is important in bidding for customer contracts. Currently those parameters are estimated from a few known fiber parameters, e.g. link length and fiber type, with a large safety margin on top. This margin could be substantially lowered with more successful bidding if the link parameters, e.g. quality of link, would be known more accurately.
Classical training aided channel estimation as used in data transmission with adaptive equalization is system optimized for maximum spectral efficiency and low overhead due to the added training sequence. Consequently either a low repetition rate of the training sequence with limited tracking speed of time-varying channel variations or short sequences for only short CIR lengths occur. Thus, low spectral resolution of channel transfer function results. The training sequence constellation is limited to signal constellation points of the payload data modulation (modulator requirement). Guard intervals are used even for cyclic training sequences in order to separate the training sequence and payload data. Training sequence synchronization is used and faulty synchronization limits the maximum tolerable CIR for the channel estimation. Carrier frequency synchronization between signal carrier and local oscillator is used to perform a reliable estimation. This can be achieved digitally with considerable effort.
However, in classical training aided channel estimation, each estimation provides an instantaneous maximum-likelihood (ML) channel characterization within the bandwidth of the digital coherent receiver. This bandwidth is defined by the analog electrical circuitry before analog/digital conversion (ADC) and by the sampling rate of the ADC.
The length of the training sequence defines the resolution of the estimated channel transfer function with longer training sequences having higher resolution. The estimation error is defined by the signal-to-noise ratio (SNR) of the channel. In slowly time-varying or static channel scenarios, averaging can further suppress the influence of noise in the channel.
Sinusoidal tone sweeping can cover a wide spectral range and provides the channel estimation of a single frequency component. A wide range of further methods is often not able to fully characterize the channel with respect to amplitude, phase and polarization or to provide accurate channel estimation.